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Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment

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  • L. G. Pee
  • Shan L. Pan
  • Lili Cui

Abstract

Knowledge embodiment, taking a social informatics perspective, refers to the transformation of knowledge into a form in which its value becomes evident. Knowledge embodiment in robotic systems with artificial intelligence (AI robotic systems) actualizes the value of knowledge much more powerfully than other entities, potentially altering the connections among people or even displacing professionals. To understand the effects of knowledge embodiment in AI robotic systems on connections among people and technology, this study addresses 2 cumulative research questions: (i) What is the nature of knowledge embodiment, that is, how are knowledge and AI robots assembled for knowledge work? (ii) How does knowledge embodiment affect connections among people and technology (that is, social informatics)? A case study of a large hospital that has employed different AI robotic systems in many parts of its healthcare service provision process indicates 4 forms of knowledge embodiment, each with a distinct focus. Further, a social informatics analysis suggests four ways knowledge embodiment affects connections among people and technology and reveals related social and institutional issues that go beyond technological determinism. Implications of these findings for research on social informatics and information science are discussed.

Suggested Citation

  • L. G. Pee & Shan L. Pan & Lili Cui, 2019. "Artificial intelligence in healthcare robots: A social informatics study of knowledge embodiment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(4), pages 351-369, April.
    • Handle: RePEc:bla:jinfst:v:70:y:2019:i:4:p:351-369
    DOI: 10.1002/asi.24145
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    Cited by:

    1. Tara Qian Sun, 2021. "Adopting Artificial Intelligence in Public Healthcare: The Effect of Social Power and Learning Algorithms," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    2. Sangseok You & Lionel P. Robert, 2023. "Subgroup formation in human–robot teams: A multi‐study mixed‐method approach with implications for theory and practice," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 323-338, March.
    3. Cao, Guangming & Duan, Yanqing & Edwards, John S. & Dwivedi, Yogesh K., 2021. "Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making," Technovation, Elsevier, vol. 106(C).
    4. Carsten Østerlund & Mohammad Hossein Jarrahi & Matthew Willis & Karen Boyd & Christine T. Wolf, 2021. "Artificial intelligence and the world of work, a co‐constitutive relationship," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 128-135, January.
    5. Zahlan, Ahmed & Ranjan, Ravi Prakash & Hayes, David, 2023. "Artificial intelligence innovation in healthcare: Literature review, exploratory analysis, and future research," Technology in Society, Elsevier, vol. 74(C).

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